As a recent approach, several studies [1]}, [2]}, [3]} propose to leverage the generality of natural language texts (i.e., title and description text of items, called item text) for bridging the domain gap in recommender systems.
The basic idea is to employ the learned text encodings via pre-trained language models (PLM) [4]}, [5]} as universal item representations.
Based on such item representations, sequential recommenders pre-trained on the interaction data from a mixture of multiple domains [1]}, [2]}, [3]} have shown promising transferability.
Such a paradigm can be denoted as “text \(\Longrightarrow \)  representation”.
Despite the effectiveness, we argue that the binding between item text and item representations is “too tight” in previous approaches [1]}, [2]}, thus leading to two potential issues.
Firstly, since these methods employ text encodings to derive item representations (without using item IDs), text semantics have a direct influence on the recommendation model. Thus, the recommender might over-emphasize the effect of text features (e.g., generating very similar recommendations in texts) instead of sequential characteristics reflected in interaction data. Secondly, text encodings from different domains (with varied distributions and semantics [11]}, [2]}) are not naturally aligned in a unified semantic space, and
the domain gap existing in text encodings is likely to cause a performance drop during multi-domain pre-training.
The tight binding between text encodings and item representations might exaggerate the negative impact of the domain gap.
